{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "26874492",
   "metadata": {},
   "source": [
    "### 案例实现思路：\n",
    "\n",
    "* 1. 加载数据集、预处理\n",
    "\n",
    "* 2. 特征工程\n",
    "\n",
    "* 3. 构建模型\n",
    "\n",
    "* 4. 模型编译、训练、验证\n",
    "\n",
    "* 5. 模型测试\n",
    "\n",
    "* 6. 结果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e40ea642",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential, layers, utils\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "041f0b3f",
   "metadata": {},
   "source": [
    "### 第1步：加载数据集、预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea868f2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据集\n",
    "\n",
    "dataset = pd.read_csv('DOM_hourly.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "797ea8e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(116189, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示shape \n",
    "    \n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7fff486e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime</th>\n",
       "      <th>DOM_MW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-12-31 01:00:00</td>\n",
       "      <td>9389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-12-31 02:00:00</td>\n",
       "      <td>9070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-12-31 03:00:00</td>\n",
       "      <td>9001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-12-31 04:00:00</td>\n",
       "      <td>9042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-12-31 05:00:00</td>\n",
       "      <td>9132.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Datetime  DOM_MW\n",
       "0  2005-12-31 01:00:00  9389.0\n",
       "1  2005-12-31 02:00:00  9070.0\n",
       "2  2005-12-31 03:00:00  9001.0\n",
       "3  2005-12-31 04:00:00  9042.0\n",
       "4  2005-12-31 05:00:00  9132.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 默认显示前5行\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f267c207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DOM_MW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>116189.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10949.203625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2413.946569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1253.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9322.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10501.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12378.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21651.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              DOM_MW\n",
       "count  116189.000000\n",
       "mean    10949.203625\n",
       "std      2413.946569\n",
       "min      1253.000000\n",
       "25%      9322.000000\n",
       "50%     10501.000000\n",
       "75%     12378.000000\n",
       "max     21651.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示数据描述\n",
    "\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b87da45e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Datetime     object\n",
       "DOM_MW      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示字段数据类型\n",
    "\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "052f9886",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将字段Datetime数据类型转换为日期类型\n",
    "\n",
    "dataset['Datetime'] = pd.to_datetime(dataset['Datetime'], format=\"%Y-%m-%d %H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fcba8434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Datetime    datetime64[ns]\n",
       "DOM_MW             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 再次查看字段的数据类型\n",
    "\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "92c70bd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将字段Datetime设置为索引列\n",
    "# 目的：后续基于索引来进行数据集的切分\n",
    "\n",
    "dataset.index = dataset.Datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7a23c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime</th>\n",
       "      <th>DOM_MW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-12-31 01:00:00</th>\n",
       "      <td>2005-12-31 01:00:00</td>\n",
       "      <td>9389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 02:00:00</th>\n",
       "      <td>2005-12-31 02:00:00</td>\n",
       "      <td>9070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 03:00:00</th>\n",
       "      <td>2005-12-31 03:00:00</td>\n",
       "      <td>9001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 04:00:00</th>\n",
       "      <td>2005-12-31 04:00:00</td>\n",
       "      <td>9042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 05:00:00</th>\n",
       "      <td>2005-12-31 05:00:00</td>\n",
       "      <td>9132.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Datetime  DOM_MW\n",
       "Datetime                                       \n",
       "2005-12-31 01:00:00 2005-12-31 01:00:00  9389.0\n",
       "2005-12-31 02:00:00 2005-12-31 02:00:00  9070.0\n",
       "2005-12-31 03:00:00 2005-12-31 03:00:00  9001.0\n",
       "2005-12-31 04:00:00 2005-12-31 04:00:00  9042.0\n",
       "2005-12-31 05:00:00 2005-12-31 05:00:00  9132.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示默认前5行\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cce621da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将原始的Datetime字段列删除\n",
    "\n",
    "dataset.drop(columns=['Datetime'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "269624c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>DOM_MW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-12-31 01:00:00</th>\n",
       "      <td>9389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 02:00:00</th>\n",
       "      <td>9070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 03:00:00</th>\n",
       "      <td>9001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 04:00:00</th>\n",
       "      <td>9042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 05:00:00</th>\n",
       "      <td>9132.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                     DOM_MW\n",
       "Datetime                   \n",
       "2005-12-31 01:00:00  9389.0\n",
       "2005-12-31 02:00:00  9070.0\n",
       "2005-12-31 03:00:00  9001.0\n",
       "2005-12-31 04:00:00  9042.0\n",
       "2005-12-31 05:00:00  9132.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 默认显示前5行\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d126a388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化显示DOM_MW的数据分布情况\n",
    "\n",
    "dataset['DOM_MW'].plot(figsize=(16,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f1717590",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据进行归一化\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "dataset['DOM_MW'] = scaler.fit_transform(dataset['DOM_MW'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "97b911c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DOM_MW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-12-31 01:00:00</th>\n",
       "      <td>0.398863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 02:00:00</th>\n",
       "      <td>0.383224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 03:00:00</th>\n",
       "      <td>0.379841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 04:00:00</th>\n",
       "      <td>0.381851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31 05:00:00</th>\n",
       "      <td>0.386263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       DOM_MW\n",
       "Datetime                     \n",
       "2005-12-31 01:00:00  0.398863\n",
       "2005-12-31 02:00:00  0.383224\n",
       "2005-12-31 03:00:00  0.379841\n",
       "2005-12-31 04:00:00  0.381851\n",
       "2005-12-31 05:00:00  0.386263"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 均值为0，标准差为1\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "100fe2bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化显示DOM_MW的数据分布情况\n",
    "\n",
    "dataset['DOM_MW'].plot(figsize=(16,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a013e8d",
   "metadata": {},
   "source": [
    "### 第2步：特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32e24764",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 功能函数：构造特征数据集和标签集\n",
    "\n",
    "def create_new_dataset(dataset, seq_len = 12):\n",
    "    '''基于原始数据集构造新的序列特征数据集\n",
    "    Params:\n",
    "        dataset : 原始数据集\n",
    "        seq_len : 序列长度（时间跨度）\n",
    "    \n",
    "    Returns:\n",
    "        X, y\n",
    "    '''\n",
    "    X = [] # 初始特征数据集为空列表\n",
    "    y = [] # 初始标签数据集为空列表\n",
    "    \n",
    "    start = 0 # 初始位置\n",
    "    end = dataset.shape[0] - seq_len # 截止位置\n",
    "    \n",
    "    for i in range(start, end): # for循环构造特征数据集\n",
    "        sample = dataset[i : i+seq_len] # 基于时间跨度seq_len创建样本\n",
    "        label = dataset[i+seq_len] # 创建sample对应的标签\n",
    "        X.append(sample) # 保存sample\n",
    "        y.append(label) # 保存label\n",
    "    \n",
    "    # 返回特征数据集和标签集\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "17dd143e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 功能函数：基于新的特征的数据集和标签集，切分：X_train, X_test\n",
    "\n",
    "def split_dataset(X, y, train_ratio=0.8):\n",
    "    '''基于X和y，切分为train和test\n",
    "    Params:\n",
    "        X : 特征数据集\n",
    "        y : 标签数据集\n",
    "        train_ratio : 训练集占X的比例\n",
    "    \n",
    "    Returns:\n",
    "        X_train, X_test, y_train, y_test\n",
    "    '''\n",
    "    X_len = len(X) # 特征数据集X的样本数量\n",
    "    train_data_len = int(X_len * train_ratio) # 训练集的样本数量\n",
    "    \n",
    "    X_train = X[:train_data_len] # 训练集\n",
    "    y_train = y[:train_data_len] # 训练标签集\n",
    "    \n",
    "    X_test = X[train_data_len:] # 测试集\n",
    "    y_test = y[train_data_len:] # 测试集标签集\n",
    "    \n",
    "    # 返回值\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f88e6f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 功能函数：基于新的X_train, X_test, y_train, y_test创建批数据(batch dataset)\n",
    "\n",
    "def create_batch_data(X, y, batch_size=32, data_type=1):\n",
    "    '''基于训练集和测试集，创建批数据\n",
    "    Params:\n",
    "        X : 特征数据集\n",
    "        y : 标签数据集\n",
    "        batch_size : batch的大小，即一个数据块里面有几个样本\n",
    "        data_type : 数据集类型（测试集表示1，训练集表示2）\n",
    "   \n",
    "    Returns:\n",
    "        train_batch_data 或 test_batch_data\n",
    "    '''\n",
    "    if data_type == 1: # 测试集\n",
    "        dataset = tf.data.Dataset.from_tensor_slices((tf.constant(X), tf.constant(y))) # 封装X和y，成为tensor类型\n",
    "        test_batch_data = dataset.batch(batch_size) # 构造批数据\n",
    "        # 返回\n",
    "        return test_batch_data\n",
    "    else: # 训练集\n",
    "        dataset = tf.data.Dataset.from_tensor_slices((tf.constant(X), tf.constant(y))) # 封装X和y，成为tensor类型\n",
    "        train_batch_data = dataset.cache().shuffle(1000).batch(batch_size) # 构造批数据\n",
    "        # 返回\n",
    "        return train_batch_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ac80fe42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ① 原始数据集\n",
    "\n",
    "dataset_original = dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f0baf28e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始数据集:  (116189, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"原始数据集: \", dataset_original.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f999fb3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ② 构造特征数据集和标签集，seq_len序列长度为12小时\n",
    "\n",
    "SEQ_LEN = 20 # 序列长度\n",
    "\n",
    "X, y = create_new_dataset(dataset_original.values, seq_len = SEQ_LEN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e084cfa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(116169, 20, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "75360903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(116169, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bfae1c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.39886263],\n",
       "       [0.38322385],\n",
       "       [0.37984116],\n",
       "       [0.38185116],\n",
       "       [0.38626336],\n",
       "       [0.39709775],\n",
       "       [0.41518776],\n",
       "       [0.43827826],\n",
       "       [0.45754486],\n",
       "       [0.47372291],\n",
       "       [0.47455633],\n",
       "       [0.46303559],\n",
       "       [0.44460241],\n",
       "       [0.4206785 ],\n",
       "       [0.40445142],\n",
       "       [0.39910776],\n",
       "       [0.4164624 ],\n",
       "       [0.47651731],\n",
       "       [0.49068536],\n",
       "       [0.47519365]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 样本1 - 特征数据\n",
    "\n",
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "184aa346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.46450632])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 样本1 - 标签\n",
    "\n",
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "91ded253",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ③ 数据集切分\n",
    "\n",
    "X_train, X_test, y_train, y_test = split_dataset(X, y, train_ratio=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8027c1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104552, 20, 1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ba6c26ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11617, 20, 1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a32b5e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104552, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ea03693a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11617, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "31af1ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ④ 基于新的X_train, X_test, y_train, y_test创建批数据(batch dataset)\n",
    "\n",
    "# 测试批数据\n",
    "\n",
    "test_batch_dataset = create_batch_data(X_test, y_test, batch_size=256, data_type=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b3792fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练批数据\n",
    "\n",
    "train_batch_dataset = create_batch_data(X_train, y_train, batch_size=256, data_type=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08da2de4",
   "metadata": {},
   "source": [
    "### 第3步：构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cabbeacc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    layers.LSTM(8, input_shape=(SEQ_LEN, 1)),\n",
    "    layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c9704772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD/CAYAAAA346CwAAAABmJLR0QA/wD/AP+gvaeTAAAbrklEQVR4nO3deVBUV9oG8KebbhAabJZBVMQ9LrEUR3QUI4Vg3BINCYPiBo5xi87EqIXLqKWWMRoj0UqiBnVqJrHKBbVKSkdN1EQzUaAGDerEBNcio+KCCw4IItDv94dFf7bdKGDTVznPr+pW2adP3/ueaz/07XPhXp2ICIiovtuh17oCInINhp1IEQw7kSIYdiJFGJ5syMjIwKpVq7SohYicZMeOHXZtdp/sly9fxs6dO11SEBE515UrV6rMr90neyVHPxmI6MW2fft2xMfHO3yO39mJFMGwEymCYSdSBMNOpAiGnUgRDDuRIhh2IkUw7ESKYNiJFMGwEymCYSdSBMNOpAiGnUgRzx325ORk6HQ66HQ6NGvWzBk1kQa8vb2t/4+VS3JystZl1Up9GoszPXfYk5KSICIIDQ11Rj2aKyoqwiuvvIIhQ4ZoXYpLFRUVITs7GwAQExMDEUFSUpLGVdVOfRqLM70wh/He3t7o06eP1mVARGCxWGCxWLQu5ZlelH2mBZXHXltVXrxCVT4+Prh48aLWZRA53QvzyU5EdatOw15aWoqFCxeiQ4cO8PLygr+/P4YOHYrdu3ejoqICwP9P8N2/fx/Hjh2zTqgYDI8OOtLS0mwmWn777TfEx8fDx8cHAQEBSEhIwN27d5Gbm4uhQ4fCx8cHTZo0wcSJE1FYWFijep/c1oMHDxy25+bmIj4+Hr6+vggICMCQIUNsjgaenLTMyspCv3794OPjAy8vL0RFReHYsWPW/kuXLrX2f/zQ9JtvvrG2/+53v7Nbf1X7zJlUGHt5eTlSU1PRv39/NG7cGJ6enujcuTM+++wz69e5goICu0m/pUuXWl//eHtcXJx13fn5+Zg2bRpatmwJd3d3BAYGIjY2FidPnqxyH589exbDhw9HQECAte3WrVu1Hp+VPCE1NVUcND9TaGioBAcH27RNmDBBzGazHDhwQIqLi+X69euSlJQkAOTw4cM2fU0mk7z22mtVrj8mJkYASGxsrBw/flyKiopk06ZNAkAGDx4sMTExkp2dLYWFhZKSkiIAZMaMGTUex+PbKikpcdgeExMj6enpUlRUJAcPHhRPT0/p0aOHw31iMpkkPDzc2j8rK0u6dOki7u7ucuTIkWrtg7CwMAkICLBrf9Y+i4qKEn9/f8nIyKjWuLOzs63je9LLNvanjeVJe/bsEQCybNkyuXPnjuTn58vnn38uer1ekpKSbPoOHDhQ9Hq9XLhwwW494eHhsnnzZuvjvLw8adGihQQFBcnevXulsLBQfv75Z4mMjJQGDRpIenq6zesr93FkZKQcPnxY7t+/L5mZmeLm5ib5+fnPHIfIU/O7vU7D3qpVK+ndu7dd33bt2tU67Hv37rVp79SpkwCQH374wW7b7du3r+EobLdVVdj37Nlj0x4XFycA7P5DQkNDBYBkZ2fbtJ8+fVoASGhoqE27s9/wkZGR4ufnZ/emqkp1wv6yjL2mYe/bt69d+5gxY8RoNMq9e/esbd9++60AkKlTp9r0PXr0qAQHB8vDhw+tbWPHjhUANj8ARESuXbsmHh4eEhYWZtNeuY/37dv3zJqr8rSw1+lh/KBBg5Ceno5JkyYhMzPTeuh+9uxZ9O3bt1br7N69u83jpk2bOmwPDg5GXl5erbbxLD169LB5HBISAgAOt2cymdC1a1ebts6dO6Np06Y4deoUrl27Vic1AsCRI0dw584dhIeHO22dL8vYa2LIkCE4fPiwXXtoaCjKyspw5swZa9uAAQPQuXNnfPXVV7h9+7a1feXKlXj//fdhNBqtbWlpadDr9XancRs3boxOnTrhxIkTuHLlit12//CHPzhjWHbqNOxr167Fpk2bcOnSJfTr1w8NGzbEoEGDsGvXrlqvs2HDhjaP9Xo93Nzc4OXlZdPu5uZWZ6fPzGazzWN3d3cAcLg9X19fh+to1KgRAODmzZtOrq5u1cex37t3DwsXLkTnzp3h5+dn/Z48a9YsAEBxcbFN/+nTp6O4uBjr1q0DAJw7dw7ff/89Jk2aZO1TWlqKe/fuwWKxwGw2233f/+mnnwAA58+ft6vHZDLVyTjrNOw6nQ4JCQk4dOgQCgoKkJaWBhFBbGys3V1ndDpdXZaimdu3b0Mc3BW78o1e+cYHHv3gevjwoV3fgoICh+t+0ffZyzL2oUOH4sMPP8TEiRNx7tw5WCwWiAhWr14NAHZjGD16NIKCgrBmzRqUlpbi008/xdixY+Hn52ft4+HhAV9fXxgMBpSVlUFEHC5RUVFOG8ez1GnYfX19kZOTAwAwGo3o37+/deZx7969Nn29vLxs/rPbt2+PDRs21GV5LvHgwQNkZWXZtP3nP/9BXl4eQkND0aRJE2t7kyZNcPXqVZu+169fx3//+1+H637R99mLPnaDwYAzZ87g2LFjaNy4MaZNm4bAwEDrD5KSkhKHr/Pw8MDUqVNx8+ZNfPrpp9i8eTM++OADu36xsbEoLy+3OftQacWKFWjevDnKy8trVPPzqPPz7O+99x5Onz6N0tJS3Lx5E5988glEBNHR0Tb9unXrhnPnzuHy5cvIyMjApUuXEBERUdfl1Tmz2Yx58+YhIyMD9+/fx/HjxzFmzBi4u7vjs88+s+k7YMAA5OXlYc2aNSgqKsLFixfxwQcf2HwCPu5Z+yw6OhoBAQHIzMys0zFWRcuxV5ebmxv69u2L69evY+XKlbh16xZKSkpw+PBhpKSkVPm6qVOnwtPTEwsWLMDrr7+Otm3b2vVZvnw52rRpg3fffRf79+/HvXv3cOfOHaxfvx5LlixBcnJynZwurVINZvMcWrlypQCwWebPny8iIidPnpTJkydLx44dxcvLS/z9/aVXr16yceNGsVgsNuvJycmRiIgIMZlMEhISImvXrhURkYyMDIfrz8rKsmtfvny5/Pjjj3btixYtqtZYdu3aZffa0aNHV1mDPDq+s1nefPNN6/oqz1D88ssvMnDgQPHx8RFPT0+JjIyUo0eP2m2/oKBAJkyYIE2aNBFPT0/p06ePZGVlSVhYmHX9c+bMeeY+qxQREVHt2XiTyWQ3lpUrV76UY3c0lqqWX3/9VfLz82Xy5MkSEhIiRqNRgoKC5E9/+pPMnTvX2u/JmXMRkYkTJzo8E/S427dvy8yZM6V169ZiNBolMDBQBgwYIAcPHrT2cbSPa5LBx7nk1BvZc3Q6UhUqjP3vf/+7wx8CWtLs1BtRfZaSkoKZM2dqXUa1MexE1fS3v/0N77zzDoqKipCSkoK7d+9i+PDhWpdVbcqE/cnznI6WxYsXO2Vblb+/ferUKVy9ehU6nQ4LFixwyrpfdPV97GlpafDz88OXX36Jbdu2uXaC7TnpRGxPIlbe31kcnB8lohfbU/K7Q5lPdiLVMexEimDYiRTBsBMpgmEnUgTDTqQIhp1IEQw7kSIYdiJFMOxEimDYiRTBsBMpgmEnUkSVf583bNgwV9ZBRE7g6Dr0lew+2UNCQmzuVUX1S35+Pv71r39pXQbVkWbNmlWZX7u/Z6f6jdcrUBb/np1IFQw7kSIYdiJFMOxEimDYiRTBsBMpgmEnUgTDTqQIhp1IEQw7kSIYdiJFMOxEimDYiRTBsBMpgmEnUgTDTqQIhp1IEQw7kSIYdiJFMOxEimDYiRTBsBMpgmEnUgTDTqQIhp1IEQw7kSIYdiJFMOxEimDYiRTBsBMpgmEnUgTDTqQIhp1IEQatC6C6c+XKFYwdOxYVFRXWtlu3bsFgMKBv3742fdu3b4/169e7uEJyJYa9HmvWrBlyc3Nx6dIlu+d++OEHm8cRERGuKos0wsP4ei4xMRFGo/GZ/UaMGOGCakhLDHs9N3r0aJSVlT21z6uvvopOnTq5qCLSCsNez7Vt2xZdunSBTqdz+LzRaMTYsWNdXBVpgWFXQGJiItzc3Bw+V15ejuHDh7u4ItICw66AkSNHwmKx2LXrdDr07NkTLVu2dH1R5HIMuwKaNm2K3r17Q6+3/e92c3NDYmKiRlWRqzHsikhISLBrExH88Y9/1KAa0gLDrohhw4bZfLK7ubnh9ddfR6NGjTSsilyJYVeEn58fBgwYYJ2oExGMGTNG46rIlRh2hYwZM8Y6UWcwGPDWW29pXBG5EsOukLfeegseHh7Wfzds2FDjisiVlPnd+CtXriA9PV3rMjTXrVs3pKeno1WrVti+fbvW5WhOpd8x0ImIaF2EK2zfvh3x8fFal0EvGEXe/gCwQ7nDeBFRenn48CFmz56teR1aL6mpqVq/FV1OubCrzmg0YvHixVqXQRpg2BXk6empdQmkAYadSBEMO5EiGHYiRTDsRIpg2IkUwbATKYJhJ1IEw06kCIadSBEMO5EiGHYiRTDsVUhOToZOp4NOp0OzZs20LsepvL29rWOrXJKTk5/6moqKCqSkpKB3794wm80wGo1o2rQp3njjDaxZswa5ubnWvl27drVb/9OWuXPn2rVlZGQ8cxyzZs2yec3SpUufd9fUb6KI1NRUqc1wQ0NDJTg4uA4q0lZ2drYAkJiYmGr1HzlypOj1elmxYoVcvnxZSkpK5MKFCzJv3jzR6XQSEBBg7RsaGio7duywef3kyZMFgOzfv9+mPT4+Xj788EObmgDI4MGDn1rPrVu3xNvbWwDI6NGjqzWGx9X2/fAS285P9jrm7e2NPn36aF3Gc8nKysLWrVsxfvx4zJ49G82aNUODBg3Qpk0bfPTRR5gyZYrTtuXp6YkWLVpg//79OH78eJX9Vq9ejZCQEKdtVwUMOz3TmTNnADy6h7sjT17a6eTJk4iLi6vWurdt24YFCxZYH+v1esydOxcAqjwsLygowJdffok5c+ZUaxv0CMNOzxQUFAQAOHjwoMPnIyMjcevWLadtb9y4cQgODsbu3btx+vRpu+c///xzvPHGG2jTpo3TtqkChr0WSktLsXDhQnTo0AFeXl7w9/fH0KFDsXv3blRUVAD4/wm++/fv49ixY9ZJJIPh0TU+09LSbCaXfvvtN8THx8PHxwcBAQFISEjA3bt3kZubi6FDh8LHxwdNmjTBxIkTUVhY6NLxRkREoHHjxvj2228xePBgHDlyxOG945zFw8MDs2bNgojgo48+snmuqKgIX3zxBebNm1dn26+3tJ41cBVnTtBNmDBBzGazHDhwQIqLi+X69euSlJQkAOTw4cM2fU0mk7z22mtVrj8mJkYASGxsrBw/flyKiopk06ZN1kmqmJgYyc7OlsLCQklJSREAMmPGDLv1REVFib+/v2RkZFRrXDWdoPvxxx8lJCTEOoHWqFEjGT16tGzZskXu37//zNdXNUH3ZE0mk0lERIqLiyUoKEj0er388ssv1j4ff/yxDB8+3FoTOEFXXZygq43vvvsOnTp1Qv/+/eHp6YmgoCCsXLkS7dq1q/U6x48fj7CwMJhMJiQkJKBTp07Yv38/Zs6cia5du8Lb2xuTJ09Gq1atsG/fPrvXWywW68UU60KfPn1w/vx5fP3114iJiUFJSQk2b96MUaNGoXnz5ti2bZtTt+fp6YmZM2fCYrFg2bJlAIDi4mKsXr0a8+fPd+q2VMGw18KgQYOQnp6OSZMmITMz03rofvbsWfTt27dW6+zevbvN46ZNmzpsDw4ORl5ent3rjxw5gjt37iA8PLxW268ODw8PJCYmIi0tDXfu3MF3332HESNG4Pbt2xgzZgyys7Odur2pU6ciICAAW7duxYULF7B+/Xr06tULXbp0cep2VMGw18LatWuxadMmXLp0Cf369UPDhg0xaNAg7Nq1q9brfPLuLHq9Hm5ubvDy8rJpd3Nzq9Pvy9VlMBgQHR2NrVu3Ys6cOaioqMDOnTudug1vb29Mnz4dFRUVWLRoEZKTk21m7qlmGPZa0Ol0SEhIwKFDh1BQUIC0tDSICGJjY7Fq1Sq7vi+7Y8eOWWfkHYmKigIA3L171+nbfv/992E2m7FlyxaEhobaHelQ9THsteDr64ucnBwAj67D3r9/f+vs+t69e236enl54eHDh9bH7du3x4YNG1xab20ZDAbk5ORARHDz5k1kZmY67Ff5yy+///3vnV6D2WzGzJkzYTab+an+nBj2Wnrvvfdw+vRplJaW4ubNm/jkk08gIoiOjrbp161bN5w7dw6XL19GRkYGLl26hIiICKfXEx0djYCAgCoD6QzDhw/Hli1bkJeXh9LSUuTm5iI5ORlLlixBWFgYEhMT62S7CxcuREFBAXr37l0n61eGpicDXKimp1pWrlxpPc1UucyfP19ERE6ePCmTJ0+Wjh07ipeXl/j7+0uvXr1k48aNYrFYbNaTk5MjERERYjKZJCQkRNauXSsiIhkZGQ7Xn5WVZde+fPly62mmx5dFixZZtxMRESF+fn6Snp7+zLGZTCa7dVW1/Prrr1JRUSFHjx6VpKQk6dmzpzRt2lQMBoP4+PhI9+7dZdmyZVWefvvHP/7hcL2FhYVPrWngwIFPHYOjdX7xxRfPHHslFU+9KXdjR0WGS8+g4PtBvRs7EqmKYSdSBMNOpAiGnUgRDDuRIhh2IkUw7ESKYNiJFMGwEymCYSdSBMNOpAiGnUgRDDuRIhh2IkUw7ESKYNiJFMGwEynCoHUBrrZ9+3atS6AXQHXu/17fKBf2+Ph4rUsg0oQy16CjRxS89ho9wmvQEamCYSdSBMNOpAiGnUgRDDuRIhh2IkUw7ESKYNiJFMGwEymCYSdSBMNOpAiGnUgRDDuRIhh2IkUw7ESKYNiJFMGwEymCYSdSBMNOpAiGnUgRDDuRIhh2IkUw7ESKYNiJFMGwEymCYSdSBMNOpAiGnUgRDDuRIhh2IkUw7ESKYNiJFMGwEynCoHUBVHfy8/Oxa9cum7bjx48DADZs2GDT7u3tjVGjRrmsNnI9nYiI1kVQ3SgtLUVgYCDu378PNzc3AICIQESg1///QV1ZWRkSExPx9ddfa1Uq1b0dPIyvxzw8PDBs2DAYDAaUlZWhrKwM5eXlqKiosD4uKysDAH6qK4Bhr+dGjRqFhw8fPrWPr68v+vXr56KKSCsMez0XFRWFwMDAKp83Go0YM2YMDAZO39R3DHs9p9frMWrUKLi7uzt8vqysDCNHjnRxVaQFhl0BI0eOrPJQvkmTJggPD3dxRaQFhl0BPXv2RIsWLezajUYjxo4dC51Op0FV5GoMuyISEhJgNBpt2ngIrxaGXRGjR4+2nmar1LZtW3Tp0kWjisjVGHZFdOjQAa+++qr1kN1oNGLcuHEaV0WuxLArJDEx0fqbdGVlZRg+fLjGFZErMewKGTFiBCoqKgAAYWFhaNu2rcYVkSsx7App0aIFevToAeDRpzyppd78IQxPH1FdiIuLw44dO7Quwxl21KvfkZw+fTp/QeQZ/ve//2HdunWYO3eu1qW88FavXq11CU5Vr8IeHh7OSadqiIyMxCuvvKJ1GS+8evKJbsXv7Api0NXEsBMpgmEnUgTDTqQIhp1IEQw7kSIYdiJFMOxEimDYiRTBsBMpgmEnUgTDTqQIhp1IEQz7Y7Zt2wadTgedTocGDRpoXY5LeXt7W8deuej1evj5+SE0NBRTp07FiRMntC6TngPD/pgRI0ZARJS871lRURGys7MBADExMRARlJWVIScnB0uWLEFOTg66d++OcePGobi4WONqqTYYdqqSm5sbgoKCEBMTg++//x6zZ8/GV199hZEjR6KeXOBIKQw7VdvHH3+Mnj17Yvfu3di2bZvW5VANMexUbTqdDn/5y18AAOvWrdO4GqoppcOek5ODt99+G2azGSaTCRERETh69GiV/fPz8zFt2jS0bNkS7u7uCAwMRGxsLE6ePGntk5aWZjPJlZubi/j4ePj6+iIgIABDhgzBxYsXbdZbWlqKhQsXokOHDvDy8oK/vz+GDh2K3bt3Wy/9XJMa6lKfPn0AAJmZmTZ3mOG+eQlIPQFAUlNTq93//Pnz4uvrK8HBwXLgwAEpLCyU06dPy4ABA6Rly5bi4eFh0z8vL09atGghQUFBsnfvXiksLJSff/5ZIiMjpUGDBpKenm7TPyYmRgBITEyMpKenS1FRkRw8eFA8PT2lR48eNn0nTJggZrNZDhw4IMXFxXL9+nVJSkoSAHL48OFa1xAVFSX+/v6SkZFRrX2SnZ1trbkqJSUlAkAASF5e3ku7b6ojLi5O4uLiavy6F9R2ZcM+bNgwASA7d+60ab969ap4eHjYhX3s2LECQDZv3mzTfu3aNfHw8JCwsDCb9so39J49e2za4+LiBIDk5+db21q1aiW9e/e2q7Fdu3Y2b+ia1hAZGSl+fn7VfqNXJ+zFxcV2YX8Z9011MOwvqJqG3cfHRwBIYWGh3XOdO3e2C7vZbBa9Xi/37t2z69+tWzcBIJcvX7a2Vb6hr1+/btN3xowZAkBOnTplbZsyZYoAkIkTJ0pGRoaUl5c7rLmmNdRUdcJ+8eJFASBGo1EePnxYq7peln1T38Ku5Hf20tJSFBYWokGDBvD29rZ7vlGjRnb97927B4vFArPZbPfLJz/99BMA4Pz583brMpvNNo/d3d0BABaLxdq2du1abNq0CZcuXUK/fv3QsGFDDBo0CLt27XJKDc5UOacRHh4Oo9HIffMSUTLsHh4e8PHxwYMHD1BUVGT3/J07d+z6+/r6wmAwoKysDCLicImKiqpVPTqdDgkJCTh06BAKCgqQlpYGEUFsbCxWrVrlkhqqw2KxYO3atQCAP//5zy6p62XZNy8DJcMOAIMHDwYAfPPNNzbtt27dwtmzZ+36x8bGory8HMeOHbN7bsWKFWjevDnKy8trVYuvry9ycnIAPLqVcv/+/a0z13v37nVJDdXx17/+Ff/+97/xzjvvYNiwYS6p62XZNy8FV31hqGuo4Xf2CxcuiL+/v81s/JkzZ2TgwIHSqFEju+/sN27ckDZt2kjr1q1l3759UlBQILdv35aUlBTx8vKy23bl99KSkhKb9jlz5ggAyc7OtraZzWaJjIyUU6dOyYMHD+TGjRuyePFiASBLly6tdQ3POxtfUVEhN27ckLS0NImOjhYA8u6770pxcfFLv2+qo759Z1c27CIiZ8+elbffflsaNmxoPe3zz3/+U/r162edcR4/fry1/+3bt2XmzJnSunVrMRqNEhgYKAMGDJCDBw9a+2RkZFhfW7nMnz/fWuPjy5tvvikiIidPnpTJkydLx44dxcvLS/z9/aVXr16yceNGsVgsNjVXp4ZKERER1Z6NN5lMdvXpdDoxm83SuXNnmTJlipw4caLK179s+6Y66lvY69VdXFNTU3mvN3Kayq8q9eSebzuU/c5OpBqGnUgRDDuRIhh2IkUw7ESKYNiJFMGwEymCYSdSBMNOpAiGnUgRDDuRIhh2IkUw7ESKYNiJFMGwEymCYSdSBMNOpIh6daUaImeLi4urN1eqMWhdgbOkpqZqXQLVQyEhIVqX4DT15pOdiJ6K16AjUgXDTqQIhp1IEQYA9WKqkYieKvP/AIIdUCr2NdvBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示模型结构\n",
    "\n",
    "utils.plot_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "29e7a615",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义 checkpoint，保存权重文件\n",
    "\n",
    "file_path = \"best_checkpoint.hdf5\"\n",
    "\n",
    "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=file_path, \n",
    "                                                         monitor='loss', \n",
    "                                                         mode='min', \n",
    "                                                         save_best_only=True,\n",
    "                                                         save_weights_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e587fb5e",
   "metadata": {},
   "source": [
    "### 第4步：模型编译、训练、验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "91bede62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型编译\n",
    "\n",
    "model.compile(optimizer='adam', loss=\"mae\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b4a3ece1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "409/409 [==============================] - 3s 3ms/step - loss: 0.1614 - val_loss: 0.0840\n",
      "Epoch 2/10\n",
      "409/409 [==============================] - 1s 2ms/step - loss: 0.0721 - val_loss: 0.0686\n",
      "Epoch 3/10\n",
      "409/409 [==============================] - 1s 2ms/step - loss: 0.0604 - val_loss: 0.0564\n",
      "Epoch 4/10\n",
      "409/409 [==============================] - 1s 2ms/step - loss: 0.0486 - val_loss: 0.0423\n",
      "Epoch 5/10\n",
      "409/409 [==============================] - 1s 2ms/step - loss: 0.0336 - val_loss: 0.0307\n",
      "Epoch 6/10\n",
      "409/409 [==============================] - 1s 2ms/step - loss: 0.0255 - val_loss: 0.0249\n",
      "Epoch 7/10\n",
      "409/409 [==============================] - 1s 2ms/step - loss: 0.0219 - val_loss: 0.0224\n",
      "Epoch 8/10\n",
      "409/409 [==============================] - 1s 2ms/step - loss: 0.0197 - val_loss: 0.0216\n",
      "Epoch 9/10\n",
      "409/409 [==============================] - 1s 2ms/step - loss: 0.0181 - val_loss: 0.0187\n",
      "Epoch 10/10\n",
      "409/409 [==============================] - 1s 2ms/step - loss: 0.0168 - val_loss: 0.0165\n"
     ]
    }
   ],
   "source": [
    "# 模型训练\n",
    "\n",
    "history = model.fit(train_batch_dataset,\n",
    "          epochs=10,\n",
    "          validation_data=test_batch_dataset,\n",
    "          callbacks=[checkpoint_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9598378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示 train loss 和 val loss\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(history.history['loss'], label='train loss')\n",
    "plt.plot(history.history['val_loss'], label='val loss')\n",
    "plt.title(\"LOSS\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3b9ff0cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "364/364 [==============================] - 0s 687us/step\n"
     ]
    }
   ],
   "source": [
    "# 模型验证\n",
    "\n",
    "test_pred = model.predict(X_test, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2db8e88e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11617, 1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred.shape # 预测结果的shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0880c750",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11617, 1)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape # 真值标签的shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5dfda214",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算r2\n",
    "\n",
    "score = r2_score(y_test, test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bfc9e4be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r^2 的值：  0.9610187211319297\n"
     ]
    }
   ],
   "source": [
    "print(\"r^2 的值： \", score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cffdbf29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制模型验证结果\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(y_test, label=\"True label\")\n",
    "plt.plot(test_pred, label=\"Pred label\")\n",
    "plt.title(\"True vs Pred\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c86e6525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制test中前100个点的真值与预测值\n",
    "\n",
    "y_true = y_test[:100]\n",
    "y_pred = test_pred[:100]\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(16,8))\n",
    "axes[0].plot(y_true, marker='o', color='red')\n",
    "axes[1].plot(y_pred, marker='*', color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78018fe9",
   "metadata": {},
   "source": [
    "### 第5步：模型测试"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c265a6a2",
   "metadata": {},
   "source": [
    "#### ① 预测1个样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f578b11a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选择test中的最后一个样本\n",
    "sample = X_test[-1]\n",
    "\n",
    "sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "97471de5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 20, 1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = sample.reshape(1, sample.shape[0], 1)\n",
    "\n",
    "sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8904dda3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.77019805]], dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型预测\n",
    "\n",
    "sample_pred = model.predict(sample)\n",
    "\n",
    "sample_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6dc4e87",
   "metadata": {},
   "source": [
    "#### ② 预测后续20个点的值 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7d2fb827",
   "metadata": {},
   "outputs": [],
   "source": [
    "ture_data = X_test[-1] # 真实test的最后20个数据点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "19ca55cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.77978233],\n",
       "       [0.79566624],\n",
       "       [0.81694284],\n",
       "       [0.83297382],\n",
       "       [0.84434749],\n",
       "       [0.83120894],\n",
       "       [0.79287185],\n",
       "       [0.76370232],\n",
       "       [0.73982743],\n",
       "       [0.72595352],\n",
       "       [0.70825571],\n",
       "       [0.69791156],\n",
       "       [0.70619669],\n",
       "       [0.74360231],\n",
       "       [0.81125601],\n",
       "       [0.83321894],\n",
       "       [0.84150407],\n",
       "       [0.84880871],\n",
       "       [0.83606236],\n",
       "       [0.81189332]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ture_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b3c8dc51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 1)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ture_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5123dccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7797823316011374,\n",
       " 0.7956662417884106,\n",
       " 0.8169428375330915,\n",
       " 0.8329738209628395,\n",
       " 0.8443474850475536,\n",
       " 0.8312089420531424,\n",
       " 0.7928718501813903,\n",
       " 0.7637023237572311,\n",
       " 0.739827434062163,\n",
       " 0.725953524855378,\n",
       " 0.7082557113442495,\n",
       " 0.6979115599568585,\n",
       " 0.7061966859496029,\n",
       " 0.7436023139523482,\n",
       " 0.8112560054907344,\n",
       " 0.8332189430336308,\n",
       " 0.8415040690263751,\n",
       " 0.8488087067359545,\n",
       " 0.8360623590548093,\n",
       " 0.8118933228747917]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(ture_data[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d6e95d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_next(model, sample, epoch=20):\n",
    "    temp1 = list(sample[:,0])\n",
    "    for i in range(epoch):\n",
    "        sample = sample.reshape(1, SEQ_LEN, 1)\n",
    "        pred = model.predict(sample)\n",
    "        value = pred.tolist()[0][0]\n",
    "        temp1.append(value)\n",
    "        sample = np.array(temp1[i+1 : i+SEQ_LEN+1])\n",
    "    return temp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a46334a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = predict_next(model, ture_data, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4ec44449",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(preds, color='yellow', label='Prediction')\n",
    "plt.plot(ture_data, color='blue', label='Truth')\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c3d4ceb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
